Predicting the Cognitive States of the Subjects in Functional Magnetic Resonance Imaging Signals Using the Combination of Feature Selection Strategies

The functional magnetic resonance imaging (fMRI) provides very useful information about the activities from different brain areas during a task. This information can be used to train a classifier and predict the sensory and motor functions and also different mental states of the subject’s brain in a particular task. Using a high resolution fMRI, normally the activities from many voxels are obtained with respect to time and not all of these voxels involve actively in a particular task. Here we propose a combination of feature selection strategies using an evolutionary computation algorithm and the support vector machines to find out those feature dimensions that are actively involved in representing the brain activities in a particular task. We show that using this lower dimensional space we can predict the cognitive state of the subjects in a particular task more accurately.

[1]  Shane T. Mueller,et al.  A note on ROC analysis and non-parametric estimate of sensitivity , 2005 .

[2]  A. Ishai,et al.  Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex , 2001, Science.

[3]  Sean M. Polyn,et al.  Beyond mind-reading: multi-voxel pattern analysis of fMRI data , 2006, Trends in Cognitive Sciences.

[4]  M. Balconi.  Neuropsychology of communication , 2010 .

[5]  F. Tong,et al.  Decoding reveals the contents of visual working memory in early visual areas , 2009, Nature.

[6]  A. E. Eiben,et al.  Introduction to Evolutionary Computing , 2003, Natural Computing Series.

[7]  Keinosuke Fukunaga,et al.  Introduction to statistical pattern recognition (2nd ed.) , 1990 .

[8]  Tom M. Mitchell,et al.  Learning to Decode Cognitive States from Brain Images , 2004, Machine Learning.

[9]  Tom M. Mitchell,et al.  Classifying Instantaneous Cognitive States from fMRI Data , 2003, AMIA.

[10]  David G. Stork,et al.  Pattern classification, 2nd Edition , 2000 .

[11]  S. Kullback,et al.  Information Theory and Statistics , 1959 .

[12]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[13]  F. Tong,et al.  Decoding Seen and Attended Motion Directions from Activity in the Human Visual Cortex , 2006, Current Biology.

[14]  Josef Kittler,et al.  Pattern recognition : a statistical approach , 1982 .

[15]  G. Rees,et al.  Predicting the orientation of invisible stimuli from activity in human primary visual cortex , 2005, Nature Neuroscience.

[16]  Tom W. Keller,et al.  Reading Span and the Time-course of Cortical Activation in Sentence-Picture Verification , 2001 .

[17]  C. Sherrington,et al.  On the Regulation of the Blood‐supply of the Brain , 1890, The Journal of physiology.

[18]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.